
We propose the simple but effective Voxel-MAE, with the novel masked voxel binary classification task, to effectively introduce the information of point cloud non-uniform distribution to learn representative features.

We validate the generalization ability of Voxel-MAE in unsupervised domain adaptive 3D object detection to address the domain shifts in LiDAR-based methods.

We verify our Voxel-MAE on KITTI [1]}, Waymo [2]} datasets with comprehensive experiments and effectively improves the performance of 3D object detection methods, such as SECOND [3]}, PV-RCNN [4]}, and CenterPoint [5]}.

Our Voxel-MAE will open new research area of self-supervised learning with large-scale unlabeled point clouds to ultimately enhance the perception ability of the autonomous vehicle.

